Kalman Filtering Algorithm Based on Random Design Matrices with Application to Integrated GNSS/INS Navigation

Kalman filter is widely used in the area of kinematic positioning and navigation.However,it doesn′t have the ability to resist the influence of measurement outliers,hence its performance is easy impacted by the observation outliers or kinematic state disturbing.In order to guarantee the reliability of the navigation with precise dynamic model,a model set,which contains many different observation models,is established.An improved Kalman filtering,in which the design matrix of the observational model is substituted by its expectation is proposed to control the influences of the measurement outliers.An integrated GPS/INS navigation example is given to show that the modified Kalman filtering algorithm works well.